Target Detection-Based Tree Recognition in a Spruce Forest Area with a High Tree Density—Implications for Estimating Tree Numbers

Mirzat Emin, Erpan Anwar, Suhong Liu, Bilal Emin, Maryam Mamut, Abduwali Abdukeram and Ting Liu
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Mirzat Emin: College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
Erpan Anwar: College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
Suhong Liu: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Bilal Emin: College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
Maryam Mamut: College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
Abduwali Abdukeram: College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
Ting Liu: College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China

Sustainability, 2021, vol. 13, issue 6, 1-12

Abstract: Here, unmanned aerial vehicle (UAV) remote sensing and machine vision were used to automatically, accurately, and efficiently count Tianshan spruce and improve the efficiency of scientific forest management, focusing on a typical Tianshan spruce forest on Tianshan Mountain, middle Asia. First, the UAV in the sampling area was cropped from the image, and a target-labeling tool was used. The Tianshan spruce trees were annotated to construct a data set, and four models were used to identify and verify them in three different areas (low, medium, and high canopy closures). Finally, the combined number of trees was calculated. The average accuracy of the detection frame, mean accuracy and precision (mAP), was used to determine the target detection accuracy. The Faster Region Convolutional Neural Network (Faster-RCNN) model achieved the highest accuracies (96.36%, 96.32%, and 95.54% under low, medium, and high canopy closures, respectively) and the highest mAP (85%). Canopy closure affected the detection and recognition accuracy; YOLOv3, YOLOv4, and Faster-RCNN all showed varying spruce recognition accuracies at different densities. The accuracy of the Faster-RCNN model decreased by at least 0.82%. Combining UAV remote sensing with target detection networks can identify and quantify statistics regarding Tianshan spruce. This solves the shortcomings of traditional monitoring methods and is significant for understanding and monitoring forest ecosystems.

Keywords: Tianshan spruce; target detection; UAV; forest inventory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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